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PubMedDate not supplied

Multiple test analyzer (MTA): a microcomputer program for determining preferred strategies with two diagnostic tests.

There are ten distinct management strategies in clinical situations that involve two diagnostic tests with dichotomous outcomes. The authors describe a microcomputer program, based on a previously described model, that can be used to identify test and test-treatment thresholds and to compute preferred strategies. The program provides tables and graphs of the results, which can be viewed or printed, and there is an optimization routine that facilitates comprehensive analysis. It can be used by decision-analytic researchers and policy analysts, medical educators who teach decision analysis, and clinicians who use decision analysis in their practices.

Computers↗
PubMed1976

Multiple testing for the detection of B virus antibody in specially handled rhesus monkeys after capture from virgin trapping grounds.

Eight groups of rhesus monkeys totaling over 1,000 animals were captured in the virgin trapping grounds of Jammu and Kashmir, India. Individual caging and special handling technics were utilized to prevent cross-contamination during capture, holding, and subsequent shipment to quarantine facilities in the United States. Immediately following the arrival of the monkeys, 5 consecutive blood samples were obtained at approximately 2-wk intervals, and the sera were rested for neutralizing antibody against Herpesvirus simiae. In order to assure the greatest sensitivity possible, sera were not heat-inactivated and were tested against only 10 TCID50 units of virus in addition to the more commonly used concentration of 100 TCID50 units. The first test detected 80-90% of the positive animals within each group, and only 1 seroconversion was noted after the second test. Seventy-three percent of the adults, 36.6% of the young adults, and 12.4% of the juvenile macaques were found to be antibody-positive. Considering the measures employed to prevent cross contamination, these percentages probably reflect the true prevalence of B virus infection in these rhesus monkeys at the time of their capture in the wild.

Animals↗
PubMed1992

Maintenance of wakefulness test and multiple sleep latency test. Measurement of different abilities in patients with sleep disorders.

The multiple sleep latency test and the maintenance of wakefulness test were administered on the same day to 258 consecutive patients whose clinical presentation required evaluation for excessive sleepiness. While the MSLT is the standard test for assessing excessive daytime sleepiness, the MWT may have some clinical advantage over the MSLT when the assessment of daytime alertness is the primary goal. To explore further the relationship between alertness and sleepiness, we have conducted a thorough analysis of the similarities, differences, and correlations between MWT and MSLT. The results of this study show that the coefficient of correlation between MSLT and MWT (r = 0.41), although statistically significant, accounts for less than 17 percent of the variability between the two tests. Factor analysis suggests that two factors, alertness and sleepiness, account for 91 percent of all variance. Our data demonstrate that patients with diagnosable disorders of excessive somnolence may be discordant on the two tests (eg, having low sleep latency on MSLT but high sleep latency on MWT). Specifically, we found that some patients with abnormally low MSLT scores were able to stay awake when asked to do so on the MWT, and conversely, some patients who failed to stay awake when asked to do so on the MWT were unable to fall asleep quickly on the MSLT. We conclude that the MWT and MSLT measure different abilities and that the MWT may be a useful adjuvant daytime test in many clinical situations.

Adult↗
PubMedDate not supplied

[Method for calculating the distribution of randomly expected scores in a false-true-do not know-type of test].

Multiple choice tests have been used widely in the evaluation of knowledge. The lowest passing limit is generally chosen arbitrarily. Better and more objective criteria may arise from analyzing the distribution of correct and incorrect answers as expected by chance. In order to calculate the distribution of correct answers and the difference between correct and incorrect answers (core) we propose the use of a method based on a gaussian distribution. The distribution of scores expected by chance is approximated by a gaussian distribution with a mean of zero and a standard deviation SD = square root of n(pA + pE), and the distribution of the total number of correct answers has a mean of npA and SD = square root of npApE, where n is the total number of questions, and pA and pE are the probabilities of having a correct and an incorrect answer, respectively. The formulae are applicable to questions type false/true/do not know and to the more common type of one correct in five options. Once the chance distribution is known, it can be compared with the distribution of scores or correct answers obtained, which can then be used to separate people in two groups: those that answer the test as expected or worse than expected by chance, and those that answer the test better than expected by chance. The first group should not be passed. The passing of individuals in the second group can be decided by additional criteria.

Educational Measurement↗
PubMed1992

Writing a multiple-choice test question.

Multiple-choice test questions are the most widely used and highly regarded of the presently available objective or selective test items. They can be used to test all levels of learning and are applicable to the measurement of most important educational outcomes. Although it is difficult to construct these questions well, they are versatile and can be used in settings involving large numbers of students.

Education, Medical, Graduate↗
PubMed1984

Peritz' F test: basic program of a robust multiple comparison test for statistical analysis of all differences among group means.

Peritz' F test has previously been found to be the most robust statistical multiple comparison test able to hold all comparisons among group means to a given experimentwise error rate. A BASIC program which will perform this test quickly on a desk-top microcomputer, needing fewer than 11 kbytes of RAM memory, is presented. The program is run in the author's laboratory using an inexpensive VIC-20 computer.

Computers↗